{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e66fc88c",
   "metadata": {},
   "source": [
    "# DAY54"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc51f92",
   "metadata": {},
   "source": [
    "## 一、 inception网络介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cecaa8e",
   "metadata": {},
   "source": [
    "今天我们介绍inception，也就是GoogleNet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d08bd4a",
   "metadata": {},
   "source": [
    "\n",
    "[传统计算机视觉的发展史](https://blog.csdn.net/qq_42604176/article/details/108588366?ops_request_misc=&request_id=&biz_id=102&utm_term=inceptionnet&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-6-108588366.142^v102^pc_search_result_base9&spm=1018.2226.3001.4187)\n",
    "\n",
    "\n",
    "从上面的链接，可以看到其实inceptionnet是在resnet之前的，那为什么我今天才说呢？因为他要引出我们后面的特征融合和特征并行处理这些思想。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b653fe61",
   "metadata": {},
   "source": [
    "Inception 网络，也被称为 GoogLeNet，是 Google 团队在 2014 年提出的经典卷积神经网络架构。它的核心设计理念是 “并行的多尺度融合”，通过在同一层网络中使用多个不同大小的卷积核（如 1x1、3x3、5x5）以及池化操作，从不同尺度提取图像特征，然后将这些特征进行融合，从而在不增加过多计算量的情况下，获得更丰富的特征表达。\n",
    "\n",
    "Inception 模块是 Inception 网络的基本组成单元。\n",
    "\n",
    "在同样的步长下，卷积核越小，下采样率越低，保留的图片像素越多；卷积核越大，越能捕捉像素周围的信息。\n",
    "\n",
    "一个典型的 Inception 模块包含以下几个并行的分支：\n",
    "\n",
    "- 1x1 卷积分支：用于降维，减少后续卷积操作的计算量，同时提取局部特征。​（像素下采样率低，但是可以修改通道数）\n",
    "- 3x3 卷积分支：捕捉中等尺度的特征。​\n",
    "- 5x5 卷积分支：捕捉较大尺度的特征。​\n",
    "- 池化分支：通常使用最大池化或平均池化，用于保留图像的全局信息。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10bbee42",
   "metadata": {},
   "source": [
    "## 二、 inception网络架构\n",
    "### 2.1 定义inception模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5cd16965",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "class Inception(nn.Module):\n",
    "    def __init__(self, in_channels):\n",
    "        \"\"\"\n",
    "        Inception模块初始化，实现多尺度特征并行提取与融合\n",
    "        \n",
    "        参数:\n",
    "            in_channels: 输入特征图的通道数\n",
    "        \"\"\"\n",
    "        super(Inception, self).__init__()\n",
    "        \n",
    "        # 1x1卷积分支：降维并提取通道间特征关系\n",
    "        # 减少后续卷积的计算量，同时保留局部特征信息\n",
    "        self.branch1x1 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels, 64, kernel_size=1),  # 降维至64通道\n",
    "            nn.ReLU()  # 引入非线性激活\n",
    "        )\n",
    "        \n",
    "        # 3x3卷积分支：通过1x1卷积降维后使用3x3卷积捕捉中等尺度特征\n",
    "        # 先降维减少计算量，再进行空间特征提取\n",
    "        self.branch3x3 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels, 96, kernel_size=1),  # 降维至96通道\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(96, 128, kernel_size=3, padding=1),  # 3x3卷积，保持空间尺寸不变\n",
    "            nn.ReLU()\n",
    "        )\n",
    "        \n",
    "        # 5x5卷积分支：通过1x1卷积降维后使用5x5卷积捕捉大尺度特征\n",
    "        # 较大的感受野用于提取更全局的结构信息\n",
    "        self.branch5x5 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels, 16, kernel_size=1),  # 大幅降维至16通道\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(16, 32, kernel_size=5, padding=2),  # 5x5卷积，保持空间尺寸不变\n",
    "            nn.ReLU()\n",
    "        )\n",
    "        \n",
    "        # 池化分支：通过池化操作保留全局信息并降维\n",
    "        # 增强特征的平移不变性\n",
    "        self.branch_pool = nn.Sequential(\n",
    "            nn.MaxPool2d(kernel_size=3, stride=1, padding=1),  # 3x3最大池化，保持尺寸\n",
    "            nn.Conv2d(in_channels, 32, kernel_size=1),  # 降维至32通道\n",
    "            nn.ReLU()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        前向传播函数，并行计算四个分支并在通道维度拼接\n",
    "        \n",
    "        参数:\n",
    "            x: 输入特征图，形状为[batch_size, in_channels, height, width]\n",
    "        \n",
    "        返回:\n",
    "            拼接后的特征图，形状为[batch_size, 256, height, width]\n",
    "        \"\"\"\n",
    "        # 注意，这里是并行计算四个分支\n",
    "        branch1x1 = self.branch1x1(x)  # 输出形状: [batch_size, 64, height, width]\n",
    "        branch3x3 = self.branch3x3(x)  # 输出形状: [batch_size, 128, height, width]\n",
    "        branch5x5 = self.branch5x5(x)  # 输出形状: [batch_size, 32, height, width]\n",
    "        branch_pool = self.branch_pool(x)  # 输出形状: [batch_size, 32, height, width]\n",
    "        \n",
    "        # 在通道维度(dim=1)拼接四个分支的输出\n",
    "        # 总通道数: 64 + 128 + 32 + 32 = 256\n",
    "        outputs = [branch1x1, branch3x3, branch5x5, branch_pool]\n",
    "        return torch.cat(outputs, dim=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31fd4086",
   "metadata": {},
   "source": [
    "上述模块变化为[B, C, H, W]-->[B, 256, H, W]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "abea6830",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "输入形状: torch.Size([32, 64, 28, 28])\n",
      "输出形状: torch.Size([32, 256, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "model = Inception(in_channels=64)\n",
    "input = torch.randn(32, 64, 28, 28)\n",
    "output = model(input)\n",
    "print(f\"输入形状: {input.shape}\")\n",
    "print(f\"输出形状: {output.shape}\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6b98cb2",
   "metadata": {},
   "source": [
    "inception模块中不同的卷积核和步长最后输出同样尺寸的特征图，这是经过精心设计的，才能在空间上对齐，才能在维度上正确拼接（concat）。"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "88cb499b",
   "metadata": {},
   "source": [
    "### 2.2 特征融合方法\n",
    "\n",
    "这里我们注意到，它是对把不同尺度的特征融合在一起。concat这种增加通道数的方法是一种经典的特征融合方法。通道数增加，空间尺寸（H, W）保持不变，每个通道的数值保持独立，没有加法运算。相当于把不同特征图 “并排” 放在一起，形成更 “厚” 的特征矩阵。\n",
    "\n",
    "在深度学习中，特征融合的尺度有以下方式：\n",
    "1. 逐元素相加：将相同形状的特征图对应位置的元素直接相加，比如残差连接：\n",
    "    ```python\n",
    "    output = x + self.residual_block(x)\n",
    "    ```\n",
    "不改变特征图尺寸和通道数，计算高效，但需保证输入形状一致。\n",
    "\n",
    "1. 逐元素相乘：通过乘法对特征进行权重分配，抑制无关特征，增强关键特征。比如注意力机制、门控机制（如 LSTM 中的遗忘门、输入门），例如\n",
    "    \n",
    "    ```python\n",
    "    attention = self.ChannelAttention(features)  # 生成通道权重\n",
    "    weighted_features = features * attention  # 逐元素相乘\n",
    "    ```\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "其他的特征融合方法我们后面有机会介绍\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8dbbf1e",
   "metadata": {},
   "source": [
    "### 2.3 InceptionNet网络定义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d219c50d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class InceptionNet(nn.Module):\n",
    "    def __init__(self, num_classes=10):\n",
    "        super(InceptionNet, self).__init__()\n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
    "        )\n",
    "\n",
    "        self.inception1 = Inception(64)\n",
    "        self.inception2 = Inception(256)\n",
    "\n",
    "        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
    "        self.fc = nn.Linear(256, num_classes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.inception1(x)\n",
    "        x = self.inception2(x)\n",
    "\n",
    "        x = self.avgpool(x)\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.fc(x)\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aa0af9c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 10])\n"
     ]
    }
   ],
   "source": [
    "# 创建网络实例\n",
    "model = InceptionNet()\n",
    "# 创建一个随机输入张量，模拟图像数据，这里假设输入图像是3通道，尺寸为224x224\n",
    "input_tensor = torch.randn(1, 3, 224, 224)\n",
    "# 前向传播\n",
    "output = model(input_tensor)\n",
    "print(output.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "235ebdad",
   "metadata": {},
   "source": [
    "inception网络有着很多变体，Inception v1版本就是 GoogLeNet，他还有v2到v4，还版本，还可以引入残差连接，如 Inception-ResNet-v2 在 ImageNet 上 top-1 准确率达 96.4%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3c7e120",
   "metadata": {},
   "source": [
    "多尺度融合等其他技巧我们将在科研班中展开了，因为单纯从讲义的形式来说，实在是不方便。将会在科研班的板块中，提到backbone-neck-head这样的范式架构设计，这将带你开启搭积木的道路。\n",
    "\n",
    "今天的内容稍微有点单薄，我们再补充点小知识点。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae27a444",
   "metadata": {},
   "source": [
    "## 三、卷积核的变体"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd6e3549",
   "metadata": {},
   "source": [
    "### 3.1 感受野\n",
    "我们发现，经常会有不同尺寸的卷积核来在特征图上滑动进一步提取信息，那么卷积核的尺寸如何选取比较合适呢？在思考这个问题前你需要理解下感受野的概念。\n",
    "\n",
    "感受野是指在卷积神经网络（CNN）中，神经元在原始输入图像上所对应的区域大小。通俗来说，卷积层中的每个输出特征图上的一个像素点，其信息来源于输入图像中的某个特定区域，这个区域的大小就是该像素点的感受野。\n",
    "\n",
    "假设我们有一个 3×3 的卷积核，对一张 5×5 的图像进行步长为 1 的卷积操作：\n",
    "\n",
    "输出特征图的每个像素点，都由输入图像中 3×3 的区域计算得到，因此该层的感受野为 3×3。\n",
    "如果再叠加一层 3×3 卷积（步长 1），第二层的每个像素点会融合第一层 3×3 区域的信息，而第一层的每个区域又对应原始图像的 3×3 区域，因此第二层的感受野扩展为 5×5（即 3+3-1=5）\n",
    "\n",
    "为了方便大家理解这个5怎么来的，找了一个博主的视频方便大家理解：\n",
    "[感受野的理解视频解析](https://www.bilibili.com/video/BV1tg411b7fn/?spm_id_from=333.337.search-card.all.click&vd_source=d95393f33b679c1250a26a5dfa6fd6f2)\n",
    "\n",
    "所以，在对应同等感受野的情况下，卷积核尺寸小有2个显著的优势：\n",
    "1. 能让参数变少，简化计算\n",
    "2. 能够引入更多的非线性（多经过几次激活函数），让拟合效果更好\n",
    "\n",
    "这也是为什么像 VGG 网络就用多层 3×3 卷积核替代大卷积核，平衡模型性能与复杂度 。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5c122af",
   "metadata": {},
   "source": [
    "### 3.2 卷积的变体\n",
    "\n",
    "卷积也是有很多变体的，除了我们之前说过的基础的卷积，还有空洞卷积、幻影卷积等等变体。我们以空洞卷积举例：\n",
    "\n",
    "空洞卷积（也叫扩张卷积、膨胀卷积 ），是对标准卷积的 “升级”—— 在卷积核元素间插入空洞（间隔），用 空洞率（dilation rate，记为d） 控制间隔大小。\n",
    "\n",
    "标准卷积（d=1）：卷积核元素紧密排列，直接覆盖输入特征图相邻区域。\n",
    "空洞卷积（d>1）：卷积核元素间插入 d-1 个空洞，等效扩大卷积核的 “感受野范围”，但不增加参数数量（仅改变计算时的采样间隔）。也就是无需增大卷积核尺寸或叠加多层卷积，仅通过调整 d，就能指数级提升感受野。\n",
    "\n",
    "对比池化（Pooling）或下采样，空洞卷积不丢失空间信息，能在扩大感受野的同时，维持特征图尺寸，特别适合语义分割、目标检测等需要精准像素 / 目标定位的任务。\n",
    "\n",
    "所以不同的设计，其实是为了不同的任务，比如你虽然可以捕捉不同尺度的信息，但是对于图像分类这个任务来说没用，我的核心是整个图像的类别，如果你是目标检测，对于小目标的检测中小尺度的设计就很有用。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6a12f25",
   "metadata": {},
   "source": [
    "### 3.3 空洞卷积示例"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c26ce7a",
   "metadata": {},
   "source": [
    "其实就是多了一个参数，代码上仅仅也是多了一个参数\n",
    "\n",
    "self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=2, dilation=2)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b20fd448",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "Epoch: 1, Batch: 100, Loss: 1.816\n",
      "Epoch: 1, Batch: 200, Loss: 1.501\n",
      "Epoch: 1, Batch: 300, Loss: 1.385\n",
      "Accuracy on test set: 55.62%\n",
      "Epoch: 2, Batch: 100, Loss: 1.210\n",
      "Epoch: 2, Batch: 200, Loss: 1.182\n",
      "Epoch: 2, Batch: 300, Loss: 1.115\n",
      "Accuracy on test set: 60.03%\n",
      "Epoch: 3, Batch: 100, Loss: 1.013\n",
      "Epoch: 3, Batch: 200, Loss: 0.991\n",
      "Epoch: 3, Batch: 300, Loss: 0.976\n",
      "Accuracy on test set: 65.74%\n",
      "Epoch: 4, Batch: 100, Loss: 0.877\n",
      "Epoch: 4, Batch: 200, Loss: 0.877\n",
      "Epoch: 4, Batch: 300, Loss: 0.856\n",
      "Accuracy on test set: 68.51%\n",
      "Epoch: 5, Batch: 100, Loss: 0.765\n",
      "Epoch: 5, Batch: 200, Loss: 0.767\n",
      "Epoch: 5, Batch: 300, Loss: 0.754\n",
      "Accuracy on test set: 69.82%\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "# 数据预处理\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),  # 转为张量\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))  # 归一化\n",
    "])\n",
    "\n",
    "# 加载CIFAR-10数据集\n",
    "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
    "                                        download=True, transform=transform)\n",
    "trainloader = DataLoader(trainset, batch_size=128, shuffle=True)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
    "                                       download=True, transform=transform)\n",
    "testloader = DataLoader(testset, batch_size=128, shuffle=False)\n",
    "\n",
    "# 定义含空洞卷积的CNN模型\n",
    "class SimpleCNNWithDilation(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(SimpleCNNWithDilation, self).__init__()\n",
    "        # 第一层：普通3×3卷积，捕捉基础特征\n",
    "        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)  \n",
    "        # 第二层：空洞卷积，dilation=2，感受野扩大（等效5×5普通卷积感受野）\n",
    "        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=2, dilation=2)  \n",
    "        # 第三层：普通3×3卷积，恢复特征对齐\n",
    "        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)  \n",
    "        \n",
    "        self.pool = nn.MaxPool2d(2, 2)  # 池化层\n",
    "        self.relu = nn.ReLU()\n",
    "        \n",
    "        # 全连接层，根据CIFAR-10尺寸计算：32×32→池化后16×16→...→最终特征维度需匹配\n",
    "        self.fc1 = nn.Linear(64 * 8 * 8, 256)  \n",
    "        self.fc2 = nn.Linear(256, 10)  \n",
    "\n",
    "    def forward(self, x):\n",
    "        # 输入: [batch, 3, 32, 32]\n",
    "        x = self.conv1(x)  # [batch, 16, 32, 32]\n",
    "        x = self.relu(x)\n",
    "        x = self.pool(x)   # [batch, 16, 16, 16]\n",
    "        \n",
    "        x = self.conv2(x)  # [batch, 32, 16, 16]（dilation=2 + padding=2 保持尺寸）\n",
    "        x = self.relu(x)\n",
    "        x = self.pool(x)   # [batch, 32, 8, 8]\n",
    "        \n",
    "        x = self.conv3(x)  # [batch, 64, 8, 8]\n",
    "        x = self.relu(x)\n",
    "        \n",
    "        x = x.view(-1, 64 * 8 * 8)  # 展平\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "# 初始化模型、损失函数、优化器\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = SimpleCNNWithDilation().to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 训练函数\n",
    "def train(epoch):\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        inputs, labels = data[0].to(device), data[1].to(device)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss += loss.item()\n",
    "        if i % 100 == 99:  # 每100个batch打印一次\n",
    "            print(f'Epoch: {epoch + 1}, Batch: {i + 1}, Loss: {running_loss / 100:.3f}')\n",
    "            running_loss = 0.0\n",
    "\n",
    "# 测试函数\n",
    "def test():\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for data in testloader:\n",
    "            images, labels = data[0].to(device), data[1].to(device)\n",
    "            outputs = model(images)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "    print(f'Accuracy on test set: {100 * correct / total:.2f}%')\n",
    "\n",
    "# 训练&测试流程\n",
    "for epoch in range(5):  # 简单跑5个epoch示例\n",
    "    train(epoch)\n",
    "    test()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a27eaa8",
   "metadata": {},
   "source": [
    "局部替换成空洞卷积，在不显著增加计算量的情况下，增强模型对长距离特征的捕捉能力，尤其适合想在小数据集（CIFAR-10）里尝试扩大感受野的场景。\n",
    "\n",
    "可以尝试在不同层设置不同dilation（比如dilation=[1,2,1] ），让模型从多个感受野维度提取特征。\n",
    "\n",
    "\n",
    "所以其实对于这些模块和类的参数本身能力的理解，才能帮助你更好的搭积木，而不是单纯的无脑的试，未来你做什么任务就积累这方面的能力即可。"
   ]
  }
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